The Emerging Scholarly Brain

It is now a commonplace observation that human society is becoming a coherent super-organism, and that the information infrastructure forms its emerging brain. Perhaps, as the underlying technologies are likely to become billions of times more powerful than those we have today, we could say that we are now building the lizard brain for the future organism.Comment: to appear in Future Professional Communication in Astronomy-II (FPCA-II) editors A. Heck and A. Accomazz

Can we do better than co-citations? Bringing Citation Proximity Analysis from idea to practice in research articles recommendation

In this paper, we build on the idea of Citation Proximity Analysis (CPA), originally introduced in [1], by developing a step by step scalable approach for building CPA-based recommender systems. As part of this approach, we introduce three new proximity functions, extending the basic assumption of co-citation analysis (stating that the more often two articles are co-cited in a document, the more likely they are related) to take the distance between the co-cited documents into account. Ask- ing the question of whether CPA can outperform co-citation analysis in recommender systems, we have built a CPA based recommender system from a corpus of 368,385 full-texts articles and conducted a user survey to perform an initial evaluation. Two of our three proximity functions used within CPA outperform co-citations on our evaluation dataset

What Others Say About This Work? Scalable Extraction of Citation Contexts from Research Papers

This work presents a new, scalable solution to the problem of extracting citation contexts: the textual fragments surrounding citation references. These citation contexts can be used to navigate digital libraries of research papers to help users in deciding what to read. We have developed a prototype system which can retrieve, on-demand, citation contexts from the full text of over 15 million research articles in the Mendeley catalog for a given reference research paper. The evaluation results show that our citation extraction system provides additional functionality over existing tools, has two orders of magnitude faster runtime performance, while providing a 9% improvement in F-measure over the current state-of-the-art